Chapter 5 Sample questions 1. Time-series forecasting models: a. | are useful whenever changes occur rapidly and wildly | b. | are more effective in making long-run forecasts than short-run forecasts | c. | are based solely on historical observations of the values of the variable being forecasted | d. | attempt to explain the underlying causal relationships which produce the observed outcome | e. | none of the above |

2. The forecasting technique which attempts to forecast short-run changes and makes use of economic indicators known as leading, coincident or lagging indicators is known as: a. | econometric technique | b. | time-series forecasting | c. | opinion polling | d. | barometric technique | e. | judgment forecasting |

3. The use of quarterly data to develop the forecasting model Yt = a +bYt1 is an example of which forecasting technique? a. | Barometric forecasting | b. | Time-series forecasting | c. | Survey and opinion | d. | Econometric methods based on an understanding of the underlying economic variables involved | e. | Input-output analysis |

4. The variation in an economic time-series which is caused by major expansions or contractions usually of greater than a year in duration is known as: a. | secular trend | b. | cyclical variation | c. | seasonal effect | d. | unpredictable random factor | e. | none of the above |

5. The type of economic indicator that can best be used for business forecasting is the: a. | leading indicator | b. | coincident indicator | c. | lagging indicator | d. | current business inventory indicator | e. | optimism/pessimism indicator |

6. In the first-order exponential smoothing model, the new forecast is equal to a weighted average of the old forecast and the actual value in the most recent period. a. | true | b. | false |

7. Simplified trend models are generally